Semiparametric methods combined parametric methods and nonparametric methods ,it is important in most of studies which take in it's nature more progress in the procedure of accurate statistical analysis which aim getting estimators efficient, the partial linear regression model is considered the most popular type of semiparametric models, which consisted of parametric component and nonparametric component in order to estimate the parametric component that have certain properties depend on the assumptions concerning the parametric component, where the absence of assumptions, parametric component will have several problems for example multicollinearity means (explanatory variables are interrelated to each other) , To treat this problem we use

In this research, the covariance estimates were used to estimate the population mean in the stratified random sampling and combined regression estimates. were compared by employing the robust variance-covariance matrices estimates with combined regression estimates by employing the traditional variance-covariance matrices estimates when estimating the regression parameter, through the two efficiency criteria (RE) and mean squared error (MSE). We found that robust estimates significantly improved the quality of combined regression estimates by reducing the effect of outliers using robust covariance and covariance matrices estimates (MCD, MVE) when estimating the regression parameter. In addition, the results of the simulation study proved

In the analysis of multiple linear regression, the problem of multicollinearity and auto-correlation drew the attention of many researchers, and given the appearance of these two problems together and their bad effect on the estimation, some of the researchers found new methods to address these two problems together at the same time. In this research a comparison for the performance of the Principal Components Two Parameter estimator (PCTP) and The (r-k) class estimator and the r-(k,d) class estimator by conducting a simulation study and through the results and under the mean square error (MSE) criterion to find the best way to address the two problems together. The results showed that the r-(k,d) class estimator is the best esti

Linear regression is one of the most important statistical tools through which it is possible to know the relationship between the response variable and one variable (or more) of the independent variable(s), which is often used in various fields of science. Heteroscedastic is one of the linear regression problems, the effect of which leads to inaccurate conclusions. The problem of heteroscedastic may be accompanied by the presence of extreme outliers in the independent variables (High leverage points) (HLPs), the presence of (HLPs) in the data set result unrealistic estimates and misleading inferences. In this paper, we review some of the robust

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

          في البيانات ذات الأبعاد العالية هناك مشكلة عدم معرفة اختيار المتغيرات ذات الاهمية لذلك يعد أداء التصنيف معياراً مهماً لمعرفة اهم المتغيرات الداخلة في النموذج حيث يلخص هذا البحث اداء تصنيف متغير الاستجابة للبيانات عالية الابعاد من خلال تطبيق اوزان مختلفة للاسو مع الوزن المقترح من قبل الباحث مع انموذج الانحدار اللوجستي الجزائي  وتم تطبيق هذه الازوان على بيانات حقيقية تضمنت 1

The repeated measurement design is called a complete randomized block design for repeated measurement when the subject is given the all different treatments , in this case the subject is considered as a block . Many of nonparametric methods were considered like Friedman test (1937) and Koch test(1969) and Kepner&Robinson test(1988) when the assumption of normal distribution of the data is not satisfied .as well as F test  when the assumptions of the analysis of variance is satisfied ,where the observations within blocks are assumed to be equally correlated . The purpose of this paper is to summarize the result of the simulation study for comparing these methods as well as present the suggested

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من اهداف بعض التجارب هي معرفة تاثير التسلسلات المختلفة لبعض الادوية او التغذية او تجارب التعلم. وفي بعض الاحيان قد تكون الوحدات التجريبية نادرة لهذا نقوم باستخدام الوحدات التجريبية على نحو متكرر. او بسبب الميزانية المحدودة فان صاحب التجربة يخضع كل وحدة تجريبية لاختبارات عديدة ويطلق على هذا النوع من التجارب التي يتم فيها استخدام الوحدات التجريبية (الاشخاص) Subject على نحو متكرر

   The technology of reducing dimensions and choosing variables are very important topics in statistical analysis to multivariate. When two or more of the predictor variables are linked in the complete or incomplete regression relationships, a problem of multicollinearity are occurred which consist of the breach of one basic assumptions of the ordinary least squares method with incorrect estimates results.

 There are several methods proposed to address this problem, including the partial least squares (PLS), used to reduce dimensional regression analysis. By using linear transformations that convert a set of variables associated with a high link to a set of new independent variables and unr

Abstract:                                        

   We can notice cluster data in social, health and behavioral sciences, so this type of data have a link between its observations and we can express these clusters through the relationship between measurements on units within the same group.

    In this research, I estimate the reliability function of cluster function by using the seemingly unrelate